DOI: 10.5194/hess-21-3915-2017
Scopus记录号: 2-s2.0-85021087844
论文题名: An intercomparison of approaches for improving operational seasonal streamflow forecasts
作者: Mendoza P ; A ; , Wood A ; W ; , Clark E ; , Rothwell E ; , Clark M ; P ; , Nijssen B ; , Brekke L ; D ; , Arnold J ; R
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2017
卷: 21, 期: 7 起始页码: 3915
结束页码: 3935
语种: 英语
Scopus关键词: Stream flow
; Water management
; Water supply
; Climate information
; Climate predictability
; Hierarchical ensemble
; Hydrologic conditions
; Objective approaches
; Statistical regression
; Statistical techniques
; Streamflow prediction
; Forecasting
; climate prediction
; comparative study
; ensemble forecasting
; hindcasting
; land surface
; seasonal variation
; statistical analysis
; streamflow
; teleconnection
; water supply
; watershed
; Pacific Northwest
英文摘要: For much of the last century, forecasting centers around the world have offered seasonal streamflow predictions to support water management. Recent work suggests that the two major avenues to advance seasonal predictability are improvements in the estimation of initial hydrologic conditions (IHCs) and the incorporation of climate information. This study investigates the marginal benefits of a variety of methods using IHCs and/or climate information, focusing on seasonal water supply forecasts (WSFs) in five case study watersheds located in the US Pacific Northwest region. We specify two benchmark methods that mimic standard operational approaches – statistical regression against IHCs and model-based ensemble streamflow prediction (ESP) – and then systematically intercompare WSFs across a range of lead times. Additional methods include (i) statistical techniques using climate information either from standard indices or from climate reanalysis variables and (ii) several hybrid/hierarchical approaches harnessing both land surface and climate predictability. In basins where atmospheric teleconnection signals are strong, and when watershed predictability is low, climate information alone provides considerable improvements. For those basins showing weak teleconnections, custom predictors from reanalysis fields were more effective in forecast skill than standard climate indices. ESP predictions tended to have high correlation skill but greater bias compared to other methods, and climate predictors failed to substantially improve these deficiencies within a trace weighting framework. Lower complexity techniques were competitive with more complex methods, and the hierarchical expert regression approach introduced here (hierarchical ensemble streamflow prediction – HESP) provided a robust alternative for skillful and reliable water supply forecasts at all initialization times. Three key findings from this effort are (1) objective approaches supporting methodologically consistent hindcasts open the door to a broad range of beneficial forecasting strategies; (2) the use of climate predictors can add to the seasonal forecast skill available from IHCs; and (3) sample size limitations must be handled rigorously to avoid over-trained forecast solutions. Overall, the results suggest that despite a rich, long heritage of operational use, there remain a number of compelling opportunities to improve the skill and value of seasonal streamflow predictions. © 2017 Author(s). Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79096
Appears in Collections: 气候变化事实与影响
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作者单位: Hydrometeorological Applications Program, National Center for Atmospheric Research, Boulder, CO, United States; Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, United States; Bureau of Reclamation, Boise, ID, United States; Bureau of Reclamation, Denver, CO, United States; Climate Preparedness and Resilience Programs, US Army Corps of Engineers, Seattle, WA, United States; Advanced Mining Technology Center (AMTC), Universidad de Chile, Santiago, Chile
Recommended Citation:
Mendoza P,A,, Wood A,et al. An intercomparison of approaches for improving operational seasonal streamflow forecasts[J]. Hydrology and Earth System Sciences,2017-01-01,21(7)